Development of a Machine Learning-Based Framework for Predicting Vessel Size Based on Container Capacity

Abstract

Ports are important hubs in logistics and supply chain systems, where the majority of the available data is still not being fully exploited. Container throughput is the amount of work done by the TEU and the ability to handle containers at a minimal cost. This capacity of container throughput is the most important part of the scale of services, which is a crucial factor in selecting port terminals. At the port container terminal, it is necessary to allocate an appropriate number of available quay cranes to the berth before container ships arrive at the port container terminal. Predicting the size of a ship is especially important for calculating the number of quay cranes that should be allocated to ships that will eventually dock at the port terminal. Machine learning techniques are flexible tools for utilizing and unlocking the value of the data. In this paper, we used neighborhood component analysis as a tool for feature selection and state-of-the-art machine learning algorithms for multiclass classification. The paper proposes a novel two-stage approach for estimating and predicting vessel size based on container capacity. Our proposed approach revealed seven unique features of port data, which are the essential parameters for the identification of the vessel size. We obtained the highest average classification accuracy of 97.6% with the linear support vector machine classifier. This study paves a new direction for research in port logistics incorporating machine learning.

AI-Powered Intelligent Seaport Mobility: Enhancing Container Drayage Efficiency through Computer Vision and Deep Learning

“…Several studies [17,18] have extensively explored the integration of deep learning and transfer learning techniques to enhance lane identification and parking spot detection across various contexts. These investigations [17][18][19][20] have effectively demonstrated the prowess of deep learning models, notably convolutional neural networks (CNN), in proficiently analyzing video data and accurately detecting lanes and parking spaces.…”

“…Rui Chen, Qiang Meng, and Peng Jia (2022) [9] provide a comprehensive review of container port drayage studies, discussing various methodological and technological angles and the shift toward e-commerce platforms. They emphasize the significance of container port drayage in the long-haul intermodal container transportation chain and its impact on various aspects, including economic advantages, management, environment, and social benefits [19].…”

A Systematic Review of Computer Vision and AI in Parking Space Allocation in a Seaport

“…Smart city technology, on the other hand, must be expanded to encompass seaports, which are key components of the global economy. Seaports play an important role in the transportation of products, and optimizing space allocation within them can have considerable efficiency and sustainability benefits [6]. As a result, the purpose of this research is to investigate the use of AI and CV techniques in seaport parking management and provide a thorough knowledge of their potential benefits.…”